The present invention relates in general to the identification of the operating condition of an internal combustion engine including fault conditions in the engine, and more specifically to the use of a pattern recognition system examining engine operating parameters to identify specific fault conditions in the engine.
Automotive control systems for internal combustion engines can include an electronic engine control for improved reliability, advanced functionality, and low cost. A typical electronic system includes a master engine controller and a plurality of sensors and actuators incorporated into the powertrain of a vehicle. The master engine controller processes data from the sensors using look-up tables and complex algorithms in order to determine appropriate commands for the actuators. For example, engine position, engine speed, throttle position, and exhaust gas oxygen content can be monitored in order to determine a proper spark timing and dwell for engine operation. The particular strategy employed is designed to achieve compliance with federal emission regulations while optimizing fuel economy and powertrain performance.
In addition to engine controls, several other automotive systems currently employ or are candidates for electronic controls. Such systems include anti-lock brakes, drive by wire systems, and transmission control.
Operation of these electronic control systems must be highly reliable for safety reasons. When a fault develops, it must be possible to trace the cause of the fault to the plant (i.e., powertrain), the controller, or its sensors or actuators.
Due to the inherent complexity of electronic control systems, the task of diagnosing a fault becomes very difficult. Therefore, a great deal of effort has been expended in searching for diagnosis methods which require a minimum of skill or knowledge of the service technician.
Prior art attempts to provide electronic engine control fault diagnosis have involved computerization in order to minimize the skill required to perform the diagnosis. For instance, a data acquisition system can be used to provide engine operating information to a computer which compares the information to prespecified normal data. The measured values are compared with preset reference values in order to find discrepancies which indicate possible faults. Some systems also apply specific algorithms to measured values in order to determine specific engine characteristics. For example, the computer may determine an integrated average over time or may extract particular features of a waveform for analysis.
Several prior art engine diagnosis systems have employed the use of an expert system which is a rule based system for analyzing input engine parameters according to rules describing the controlled system developed by an expert. An expert system requires an intense learning process by the expert to understand the system and to codify his knowledge into a set of rules. Thus, expert system development takes a large amount of time and resources. An expert system is not responsive to frequent design changes such as occur in automotive development. A change in engine design changes the rules, which requires the expert to determine the new rules and to redesign the system.
U.S. Pat. No. 4,252,013, issued to Hyanova et al, discloses a diagnostic system for an internal combustion engine in which pick-up devices sense engine parameters. Dynamic input signals are preprocessed in order to provide quasi-static indices of engine conditions. The preprocessed signals and other measured signals are applied to predetermined algorithms to give diagnostic indices of the internal combustion engine.
U.S. Pat. No. 4,375,672, issued to Kato et al, discloses a computerized engine analyzer for making automatic diagnosis. Sensors connected to an internal combustion engine detect characteristics of the engine. The signals from the sensors are preprocessed and are then compared in a computer with a preset program corresponding to the engine. The results of various diagnoses of the engine are indicated as the diagnosis proceeds under control of an operator.
U.S. Pat. No. 4,418,388, issued to Allgor et al, discloses an engine analyzer which measures engine operating parameters including electrical system waveforms which are then stored in the memory. The stored waveforms are compared to analytical matrices dictated by engine type. Based on the preprogrammed relationship of matrix locations and the input waveforms, information is processed to identify defective systems.
U.S. Pat. No. 4,424,709, issued to Meier Jr. et al, discloses an engine analyzer for identifying engine defects based on signals from various sensors. The detected signals are compared with signals from normal engines in the frequency domain. By recognizing specific differences, or looking for abnormal frequency components, engine defects are identified.
An expert system for car fault diagnosis is described in Armano et al, Empirical And Functional Knowledge In An Expert System For Fault Diagnosis, IEEE International Workshop on Artificial Intelligence for Industrial Applications, pages 109-114 (1988). The expert system architecture disclosed in this paper uses artificial intelligence techniques to incorporate both empirical knowledge and functional knowledge to reach a fault diagnosis.
The use of failure detection filters to diagnose automotive control systems is disclosed in Liubakka et al, Failure Detection Algorithms Applied To Control System Design For Improved Diagnostics And Reliability, SAE Technical Paper Series, Paper No. 880726 (1988). The failure detection and isolation method disclosed employs a system model for predicting system operation. Known relationships between system inputs and outputs allow the diagnostic system to anticipate a system output given detected system inputs. Any difference between the predicted system outputs and those detected by the sensors indicates a system fault.
Each of the prior art diagnostic systems requires predetermined engine analytical information in order to design a system to detect faults. As electronic systems increase in complexity, the determination of such analytical information becomes more difficult, time-consuming, and expensive. These are some of the problems this invention overcomes.